analyze_query_results
Execute a query and analyze its results using AI
LABSCO SUMMARY
It calls OpenAI itself, which is a second bill and a second data path.
The AI tools do not run on your client's model — they need your own OPENAI_API_KEY, and query text and sampled rows go to OpenAI when you use them. The plain database tools work without it. Decide which half you want before pointing this at production data, and note that read privileges are what the setup guide asks for.
What it is
A FastMCP server that pairs a Snowflake connection with an OpenAI model. The database half lists and describes objects and runs SQL; the model half turns questions into SQL, explains queries in English, and reads results back as analysis.
What you get
- Databases, schemas and tables listed, and a table described down to its columns —
list_databases,list_schemas,list_tables,describe_table - Sample rows pulled from a table, and arbitrary SQL executed with results returned —
get_table_sample,execute_sql - Warehouses listed and the current warehouse, database and schema reported —
list_warehouses,get_warehouse_status - A natural-language question turned into SQL against a named database and schema —
natural_language_to_sql - Query results analysed, queries explained in plain English, optimisations suggested, and table-level insights generated —
analyze_query_results,explain_query,suggest_query_optimizations,generate_table_insights - Four resource URIs for browsing structure directly:
snowflake://databases,snowflake://schemas/{database},snowflake://tables/{database}/{schema},snowflake://table/{database}/{schema}/{table} - Three prompt templates for analysis, exploration and optimisation —
sql_analysis_prompt,data_exploration_prompt,sql_optimization_prompt
Requirements
Snowflake credentials — SNOWFLAKE_ACCOUNT, SNOWFLAKE_USER, SNOWFLAKE_PASSWORD — with optional SNOWFLAKE_WAREHOUSE, SNOWFLAKE_DATABASE, SNOWFLAKE_SCHEMA and SNOWFLAKE_ROLE for default context. Separately, OPENAI_API_KEY is required for the AI tools, with OPENAI_MODEL defaulting to gpt-4. Python 3.11 or newer. The Snowflake user needs USAGE on warehouses, databases and schemas, SELECT on tables, and SHOW privileges to list objects.
Setup effort
Build from source — git clone https://github.com/rickyb30/datapilot-mcp-server
USE CASES
01
Finding your way around a Snowflake account you just got access to
List the databases, then schemas, then tables, and describe the ones that look relevant — the column detail comes back without writing a query.
02
Answering a business question without writing the SQL
Ask in English against a named database and schema, get the generated SQL back, then run it and have the results summarised.
03
Making a slow query faster
Get the query explained in plain English so you can see what it is really doing, then ask for optimisation suggestions against it.
04
Deciding whether a table is worth using
Pull a sample of rows and generate insights over it, instead of guessing from column names what the data actually contains.
13 tools
Counted once with only startup setup. Other versions, modes or optional credentials may expose a different list.
describe_table
Get detailed information about a table's columns
execute_sql
Execute a SQL query on Snowflake and return results
explain_query
Explain what a SQL query does in plain English
generate_table_insights
Generate AI-powered insights about a table's data
get_table_sample
Get a sample of data from a table
get_warehouse_status
Get current warehouse, database, and schema status
list_databases
List all databases available to the user
list_schemas
List all schemas in a database
list_tables
List all tables in a database/schema
list_warehouses
List all warehouses available to the user
natural_language_to_sql
Convert natural language question to SQL query using AI
suggest_query_optimizations
Get AI-powered suggestions for optimizing a SQL query
BEFORE YOU INSTALL
What to know first
Tools you get
13 tools when we connected.
Requires
- SNOWFLAKE_ACCOUNT in your environment
- SNOWFLAKE_PASSWORD in your environment
- OPENAI_API_KEY in your environment
- SNOWFLAKE_USER in your environment
Where it runs
The documented client runs the server on your machine.
CONTEXT COST
Light Standing cost
Rated by the size of the 13 tool definitions that ride along on every turn, not by how many there are
INSTALLATION
Connect your client
You clone this one yourself.
Server setup
TERMINAL
git clone https://github.com/rickyb30/datapilot-mcp-server
cd datapilot-mcp-server
python -m venv venv
source venv/bin/activate # On Windows: venv\Scripts\activate
pip install -r requirements.txt
cp env.template .env
# Edit .env with your credentialsMCP Clients
Claude Desktop
CLAUDE DESKTOP · JSON
{
"mcpServers": {
"datapilot": {
"command": "python",
"args": [
"-m",
"src.main"
],
"cwd": "<YOUR_PATH_TO_DATAPILOT>",
"env": {
"SNOWFLAKE_ACCOUNT": "<YOUR_SNOWFLAKE_ACCOUNT>",
"SNOWFLAKE_USER": "<YOUR_SNOWFLAKE_USER>",
"SNOWFLAKE_PASSWORD": "<YOUR_SNOWFLAKE_PASSWORD>",
"OPENAI_API_KEY": "<YOUR_OPENAI_API_KEY>"
}
}
}
}
